论文标题
3D分子图的几何完整感知网络
Geometry-Complete Perceptron Networks for 3D Molecular Graphs
论文作者
论文摘要
几何深度学习领域对创新和强大的图形神经网络体系结构的发展产生了深远的影响。诸如计算机视觉和计算生物学之类的学科从这种方法学的进步中受益匪浅，这导致了诸如蛋白质结构预测和设计等科学领域的突破。在这项工作中，我们介绍了GCPNET，这是一种新的几何完整，SE（3） - 等级图神经网络，设计用于3D分子图表示学习。跨四个不同几何任务进行的严格实验表明，GCPNET对蛋白质 - 配体结合亲和力的预测（1）实现了0.608的统计学意义相关性，比当前的最新方法高5％。 （2）对于蛋白质结构排名，统计学上具有显着的靶标 - 局部和数据集 - 全球相关性分别为0.616和0.871； （3）对于新区的多体系统建模实现了任务平均平方误差小于0.01，比当前方法好15％； （4）对于分子手性识别，达到98.7％的最新预测准确性，比迄今为止的任何其他机器学习方法要好得多。可以在https://github.com/bioinfolachinelearning/gcpnet上免费获得训练新模型或重现我们的结果的源代码，数据和说明。
The field of geometric deep learning has had a profound impact on the development of innovative and powerful graph neural network architectures. Disciplines such as computer vision and computational biology have benefited significantly from such methodological advances, which has led to breakthroughs in scientific domains such as protein structure prediction and design. In this work, we introduce GCPNet, a new geometry-complete, SE(3)-equivariant graph neural network designed for 3D molecular graph representation learning. Rigorous experiments across four distinct geometric tasks demonstrate that GCPNet's predictions (1) for protein-ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5% greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.
